James Briggs Courses

Composite Indexes and the Faiss Index Factory

Explore composite indexes and Faiss Index Factory for optimizing vector search performance, balancing recall, latency, and memory usage in similarity search applications.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Faiss - Vector Compression with PQ and IVFPQ in Python

Learn to implement vector compression using Product Quantization (PQ) and Inverted File Product Quantization (IVFPQ) in Faiss for efficient semantic search and similarity retrieval in large-scale datasets.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

How LSH Random Projection Works in Search - Python

Explore efficient similarity search using Locality Sensitive Hashing (LSH) and Random Projection. Learn how to handle large-scale data and implement approximate search techniques in Python.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

IndexLSH for Fast Similarity Search in Faiss

Explore fast similarity search using IndexLSH in Faiss. Learn efficient implementation techniques for large-scale data retrieval and comparison in AI applications.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

Why Are There So Many Tokenization Methods in HF Transformers?

Explore five tokenization methods in HuggingFace Transformers, comparing their outputs and understanding the differences to enhance your NLP skills and transformer model usage.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Training BERT - Masked-Language Modeling

Explore BERT's masked-language modeling technique for NLP tasks. Learn how this innovative approach trains the model to predict masked tokens, enhancing its understanding of language context.

• YouTube
• 16 minutes
• Self-Paced
• Free Video

Extractive Q&A With Haystack and FastAPI in Python

Build an extractive Q&A system using Haystack, FastAPI, and BERT transformer. Implement Elasticsearch with BM25 retriever for efficient information retrieval and answer generation from text documents.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

How to Use Type Annotations in Python

Learn to use Python type annotations for clearer, more explicit code. Explore simple and complex types, IDE warnings, union operators, and optional types to enhance code readability and maintainability.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

The New Match-Case Statement in Python 3.10

Explore Python 3.10's new match-case statement, a powerful upgrade to switch-case logic. Learn its syntax, applications, and advantages over traditional if-else structures.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Unicode Normalization for NLP in Python

Learn Unicode normalization techniques for handling text variants and diacritics in NLP, ensuring consistent and readable input for your models.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

How to Build Q&A Models in Python - Transformers

Learn to build question-answering models using HuggingFace's Transformers library in Python, covering model importing, loading, tokenization, and pipeline setup for effective Q&A systems.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

Python's Newest and Best Features

Explore six cutting-edge Python features from versions 3.7-3.9, including breakpoints, walrus operator, and dictionary unions, enhancing code efficiency and functionality.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

LangChain Data Loaders, Tokenizers, Chunking, and Datasets - Data Prep

Learn to prepare text data for LLMs using LangChain tools: data loading, tokenization, chunking, and dataset creation. Practical guide for efficient text processing in AI applications.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

ChatGPT Plugins - Build Your Own in Python

Learn to build ChatGPT plugins in Python using OpenAI's template. Covers setup, deployment, and integration with ChatGPT, focusing on a retrieval plugin for LangChain documentation.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Convolutional Neural Nets Explained and Implemented in Python - PyTorch

Comprehensive guide to Convolutional Neural Networks: theory, implementation, and practical applications using Python and PyTorch for computer vision tasks like image classification.

• YouTube
• 35 minutes
• Self-Paced
• Free Video